Cross-Lingual Word Embeddings

Research output: Book/ReportBook

The majority of natural language processing (NLP) is English language processing, and while there is good language technology support for (standard varieties of) English, support for Albanian, Burmese, or Cebuano-and most other languages-remains limited. Being able to bridge this digital divide is important for scientific and democratic reasons but also represents an enormous growth potential. A key challenge for this to happen is learning to align basic meaning-bearing units of different languages. In this book, the authors survey and discuss recent and historical work on supervised and unsupervised learning of such alignments. Specifically, the book focuses on so-called cross-lingual word embeddings. The survey is intended to be systematic, using consistent notation and putting the available methods on comparable form, making it easy to compare wildly different approaches. In so doing, the authors establish previously unreported relations between these methods and are able to present a fast-growing literature in a very compact way. Furthermore, the authors discuss how best to evaluate cross-lingual word embedding methods and survey the resources available for students and researchers interested in this topic. Table of Contents: Preface / Introduction / Monolingual Word Embedding Models / Cross-Lingual Word Embedding Models: Typology / A Brief History of Cross-Lingual Word Representations / Word-Level Alignment Models / Sentence-Level Alignment Methods / Document-Level Alignment Models / From Bilingual to Multilingual Training / Unsupervised Learning of Cross-Lingual Word Embeddings / Applications and Evaluation / Useful Data and Software / General Challenges and Future Directions / Bibliography / Authors' Biographies.

Original languageEnglish
PublisherMorgan & Claypool Publishers
Edition2
Number of pages132
ISBN (Electronic)9781681730639, 9781681735269
DOIs
Publication statusPublished - 2019
SeriesSynthesis Lectures on Human Language Technologies
ISSN1947-4040

    Research areas

  • cross-lingual learning, machine learning, natural language processing, semantics

ID: 240408154